Using Programmable Graphene Channels as Weights in Spin-Diffusive Neuromorphic Computing
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Paul A. Crowell | Yoska Anugrah | Steven J. Koester | Gordon Stecklein | S. Koester | P. Crowell | Jiaxi Hu | Jiaxi Hu | G. Stecklein | Y. Anugrah
[1] Weisheng Zhao,et al. Proposal for a graphene-based all-spin logic gate , 2015 .
[2] J. Meindl,et al. Conductance Modeling for Graphene Nanoribbon (GNR) Interconnects , 2007, IEEE Electron Device Letters.
[3] P. Ruden,et al. Rashba-induced spin scattering at graphene edges , 2013 .
[4] Jacques-Olivier Klein,et al. Analytical Macrospin Modeling of the Stochastic Switching Time of Spin-Transfer Torque Devices , 2015, IEEE Transactions on Electron Devices.
[5] Takashi Kimura,et al. Giant spin-accumulation signal and pure spin-current-induced reversible magnetization switching , 2008 .
[6] Bernard Girau,et al. Fault and Error Tolerance in Neural Networks: A Review , 2017, IEEE Access.
[7] B. Wees,et al. Eighty-Eight Percent Directional Guiding of Spin Currents with 90 μm Relaxation Length in Bilayer Graphene Using Carrier Drift. , 2016, Nano letters.
[8] Don Monroe,et al. Neuromorphic computing gets ready for the (really) big time , 2014, CACM.
[9] Sachin S. Sapatnekar,et al. CoMET: Composite-Input Magnetoelectric- Based Logic Technology , 2016, IEEE Journal on Exploratory Solid-State Computational Devices and Circuits.
[10] An Chen,et al. A Comprehensive Crossbar Array Model With Solutions for Line Resistance and Nonlinear Device Characteristics , 2013, IEEE Transactions on Electron Devices.
[11] Jonathan Z. Sun. Spin-current interaction with a monodomain magnetic body: A model study , 2000 .
[12] Jacques-Olivier Klein,et al. Spin-Transfer Torque Magnetic Memory as a Stochastic Memristive Synapse for Neuromorphic Systems , 2015, IEEE Transactions on Biomedical Circuits and Systems.
[13] Xinjie Guo,et al. Mixed Signal Neurocomputing Based on Floating-gate Memories , 2017 .
[14] P. Kim,et al. Energy band-gap engineering of graphene nanoribbons. , 2007, Physical review letters.
[15] F. Guinea,et al. Spin-orbit-mediated spin relaxation in graphene. , 2008, Physical review letters.
[16] Takashi Taniguchi,et al. Gate-tunable black phosphorus spin valve with nanosecond spin lifetimes , 2017, Nature Physics.
[17] Xinjie Guo,et al. Redesigning commercial floating-gate memory for analog computing applications , 2014, 2015 IEEE International Symposium on Circuits and Systems (ISCAS).
[18] J. Appenzeller,et al. Spin transfer torque in a graphene lateral spin valve assisted by an external magnetic field. , 2013, Nano letters.
[19] Witold Pedrycz,et al. Current-Mode Analog Adaptive Mechanism for Ultra-Low-Power Neural Networks , 2011, IEEE Transactions on Circuits and Systems II: Express Briefs.
[20] Leon O. Chua,et al. Cellular neural networks: applications , 1988 .
[21] Claudia Felser,et al. Mn3Ga, a compensated ferrimagnet with high Curie temperature and low magnetic moment for spin torque transfer applications , 2007 .
[22] Behtash Behin-Aein,et al. All-Spin Logic Device With Inbuilt Nonreciprocity , 2011, IEEE Transactions on Magnetics.
[23] Catherine D. Schuman,et al. A Survey of Neuromorphic Computing and Neural Networks in Hardware , 2017, ArXiv.
[24] Dmitri E. Nikonov,et al. Modeling and Design of Spintronic Integrated Circuits , 2012, IEEE Transactions on Circuits and Systems I: Regular Papers.
[25] W. Rippard,et al. Switching Distributions for Perpendicular Spin-Torque Devices Within the Macrospin Approximation , 2012, IEEE Transactions on Magnetics.
[26] M. Kamalakar,et al. Long distance spin communication in chemical vapour deposited graphene , 2015, Nature Communications.
[27] Shigeyoshi Watanabe,et al. A 0.67μm^2 self-aligned shallow trench isolation cell (SA-STI cell) for 3V-only 256Mbit NAND EEP-ROMs , 1994 .
[28] R. Lippmann,et al. An introduction to computing with neural nets , 1987, IEEE ASSP Magazine.
[29] Azad Naeemi,et al. Evaluation of the Potential Performance of Graphene Nanoribbons as On-Chip Interconnects , 2013, Proceedings of the IEEE.
[30] Andre K. Geim,et al. The rise of graphene. , 2007, Nature materials.
[31] R. S. Ross,et al. Ultra-low resistance ohmic contacts in graphene field effect transistors , 2012 .
[32] Azad Naeemi,et al. A Proposal for Energy-Efficient Cellular Neural Network Based on Spintronic Devices , 2016, IEEE Transactions on Nanotechnology.
[33] S. Koester,et al. Contact-Induced Spin Relaxation in Graphene Nonlocal Spin Valves , 2016 .
[34] Michael S. Fuhrer,et al. Gate-tunable graphene spin valve , 2007 .
[35] Sachin S. Sapatnekar,et al. Spin-Based Computing: Device Concepts, Current Status, and a Case Study on a High-Performance Microprocessor , 2015, Proceedings of the IEEE.
[36] Dan Hammerstrom. DARPA neurocomputing , 2015, 2015 IEEE International Electron Devices Meeting (IEDM).
[37] Dmitri E. Nikonov,et al. Material Targets for Scaling All Spin Logic , 2012, ArXiv.
[38] G. W. Burr,et al. Experimental demonstration and tolerancing of a large-scale neural network (165,000 synapses), using phase-change memory as the synaptic weight element , 2015, 2014 IEEE International Electron Devices Meeting.
[39] S. Datta,et al. Proposal for an all-spin logic device with built-in memory. , 2010, Nature nanotechnology.
[40] Spin relaxation in single-layer graphene with tunable mobility. , 2012, Nano letters.
[41] Naresh R. Shanbhag,et al. Shannon-inspired Statistical Computing to Enable Spintronics , 2017, ArXiv.
[42] S. Koester,et al. The effect of output-input isolation on the scaling and energy consumption of all-spin logic devices , 2015 .
[43] K. Roy,et al. Spin-Based Neuron Model With Domain-Wall Magnets as Synapse , 2012, IEEE Transactions on Nanotechnology.
[44] Charbel Sakr,et al. Analytical Guarantees on Numerical Precision of Deep Neural Networks , 2017, ICML.
[45] R. Hecht-Nielsen,et al. Theory of the Back Propagation Neural Network , 1989 .
[46] Naveen Verma,et al. Enabling system-level platform resilience through embedded data-driven inference capabilities in electronic devices , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[47] J. Appenzeller,et al. Improvement of spin transfer torque in asymmetric graphene devices. , 2014, ACS nano.
[48] Howard C. Card,et al. Stochastic Neural Computation I: Computational Elements , 2001, IEEE Trans. Computers.
[49] Azad Naeemi,et al. Non-Boolean Computing Benchmarking for Beyond-CMOS Devices Based on Cellular Neural Network , 2016, IEEE Journal on Exploratory Solid-State Computational Devices and Circuits.
[50] Wei Han,et al. Graphene spintronics. , 2014, Nature nanotechnology.
[51] Wei Han,et al. Tunneling spin injection into single layer graphene. , 2010, Physical review letters.
[52] Weisheng Zhao,et al. Gate-driven pure spin current in graphene , 2017, 2017 IEEE International Magnetics Conference (INTERMAG).
[53] Masafumi Yamamoto,et al. Giant tunneling magnetoresistance in epitaxial Co2MnSi/MgO/Co2MnSi magnetic tunnel junctions by half-metallicity of Co2MnSi and coherent tunneling , 2012 .
[54] Byoungil Lee,et al. Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing. , 2012, Nano letters.
[55] Dmitri E. Nikonov,et al. Static and Clocked Spintronic Circuit Design and Simulation With Performance Analysis Relative to CMOS , 2014, IEEE Transactions on Circuits and Systems I: Regular Papers.
[56] Yuyuan Tian,et al. Measurement of the quantum capacitance of graphene. , 2009, Nature nanotechnology.
[57] Hanan Dery,et al. A two-dimensional spin field-effect switch , 2016, Nature Communications.
[58] Massimiliano Di Ventra,et al. The parallel approach , 2013 .
[59] A. Geim,et al. Two-dimensional gas of massless Dirac fermions in graphene , 2005, Nature.
[60] Kaushik Roy,et al. SPINDLE: SPINtronic Deep Learning Engine for large-scale neuromorphic computing , 2014, 2014 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED).
[61] Yiran Chen,et al. Memristor Crossbar-Based Neuromorphic Computing System: A Case Study , 2014, IEEE Transactions on Neural Networks and Learning Systems.
[62] Takashi Taniguchi,et al. Spin Lifetimes Exceeding 12 ns in Graphene Nonlocal Spin Valve Devices. , 2016, Nano letters.
[63] Peter R. Kinget,et al. A programmable analog cellular neural network CMOS chip for high speed image processing , 1995, IEEE J. Solid State Circuits.
[64] Robert Hecht-Nielsen,et al. Theory of the backpropagation neural network , 1989, International 1989 Joint Conference on Neural Networks.